Overview

Brought to you by YData

Dataset statistics

Number of variables46
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory317.4 KiB
Average record size in memory221.1 B

Variable types

Numeric15
Boolean22
Categorical9

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
BusinessTravel_Travel_Frequently is highly overall correlated with BusinessTravel_Travel_RarelyHigh correlation
BusinessTravel_Travel_Rarely is highly overall correlated with BusinessTravel_Travel_FrequentlyHigh correlation
Department_Research & Development is highly overall correlated with Department_Sales and 1 other fieldsHigh correlation
Department_Sales is highly overall correlated with Department_Research & Development and 2 other fieldsHigh correlation
EducationField_Life Sciences is highly overall correlated with EducationField_MedicalHigh correlation
EducationField_Marketing is highly overall correlated with Department_SalesHigh correlation
EducationField_Medical is highly overall correlated with EducationField_Life SciencesHigh correlation
JobLevel is highly overall correlated with JobRole_Manager and 2 other fieldsHigh correlation
JobRole_Manager is highly overall correlated with JobLevel and 2 other fieldsHigh correlation
JobRole_Research Director is highly overall correlated with MonthlyIncomeHigh correlation
JobRole_Sales Executive is highly overall correlated with Department_Research & Development and 1 other fieldsHigh correlation
MaritalStatus_Married is highly overall correlated with MaritalStatus_SingleHigh correlation
MaritalStatus_Single is highly overall correlated with MaritalStatus_Married and 1 other fieldsHigh correlation
MonthlyIncome is highly overall correlated with JobLevel and 3 other fieldsHigh correlation
PercentSalaryHike is highly overall correlated with PerformanceRatingHigh correlation
PerformanceRating is highly overall correlated with PercentSalaryHikeHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatus_SingleHigh correlation
TotalWorkingYears is highly overall correlated with Age and 4 other fieldsHigh correlation
YearsAtCompany is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsAtCompany and 2 other fieldsHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
YearsWithCurrManager is highly overall correlated with YearsAtCompany and 1 other fieldsHigh correlation
EducationField_Marketing is highly imbalanced (50.6%) Imbalance
EducationField_Other is highly imbalanced (69.0%) Imbalance
EducationField_Technical Degree is highly imbalanced (56.4%) Imbalance
JobRole_Human Resources is highly imbalanced (77.9%) Imbalance
JobRole_Manager is highly imbalanced (63.6%) Imbalance
JobRole_Manufacturing Director is highly imbalanced (53.5%) Imbalance
JobRole_Research Director is highly imbalanced (69.5%) Imbalance
JobRole_Sales Representative is highly imbalanced (68.7%) Imbalance
EmployeeNumber has unique values Unique
NumCompaniesWorked has 197 (13.4%) zeros Zeros
TrainingTimesLastYear has 54 (3.7%) zeros Zeros
YearsAtCompany has 44 (3.0%) zeros Zeros
YearsInCurrentRole has 244 (16.6%) zeros Zeros
YearsSinceLastPromotion has 581 (39.5%) zeros Zeros
YearsWithCurrManager has 263 (17.9%) zeros Zeros

Reproduction

Analysis started2025-03-28 10:25:41.782657
Analysis finished2025-03-28 10:26:51.334085
Duration1 minute and 9.55 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:51.566052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2025-03-28T15:56:51.865047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1233 
True
237 
ValueCountFrequency (%)
False 1233
83.9%
True 237
 
16.1%
2025-03-28T15:56:52.120203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.48571
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:52.386202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5091
Coefficient of variation (CV)0.50282403
Kurtosis-1.2038228
Mean802.48571
Median Absolute Deviation (MAD)344
Skewness-0.0035185684
Sum1179654
Variance162819.59
MonotonicityNot monotonic
2025-03-28T15:56:52.697163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
408 5
 
0.3%
530 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
829 4
 
0.3%
1469 4
 
0.3%
267 4
 
0.3%
217 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:52.985163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2025-03-28T15:56:53.268355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 211
14.4%
1 208
14.1%
10 86
 
5.9%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1 208
14.1%
2 211
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.9%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 28
1.9%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2025-03-28T15:56:53.549351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:56:53.802851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

EmployeeNumber
Real number (ℝ)

Unique 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:54.117855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityStrictly increasing
2025-03-28T15:56:54.440691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1391 1
 
0.1%
1389 1
 
0.1%
1387 1
 
0.1%
1383 1
 
0.1%
1382 1
 
0.1%
1380 1
 
0.1%
1379 1
 
0.1%
1377 1
 
0.1%
1375 1
 
0.1%
Other values (1460) 1460
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
2068 1
0.1%
2065 1
0.1%
2064 1
0.1%
2062 1
0.1%
2061 1
0.1%
2060 1
0.1%
2057 1
0.1%
2056 1
0.1%
2055 1
0.1%
2054 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Length

2025-03-28T15:56:54.742260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:56:54.989254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891156
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:55.314254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.329428
Coefficient of variation (CV)0.30853044
Kurtosis-1.1963985
Mean65.891156
Median Absolute Deviation (MAD)18
Skewness-0.032310953
Sum96860
Variance413.28563
MonotonicityNot monotonic
2025-03-28T15:56:55.648253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
98 28
 
1.9%
42 28
 
1.9%
48 28
 
1.9%
84 28
 
1.9%
57 27
 
1.8%
79 27
 
1.8%
96 27
 
1.8%
54 26
 
1.8%
52 26
 
1.8%
Other values (61) 1196
81.4%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2025-03-28T15:56:55.975000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:56:56.220995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

JobLevel
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2025-03-28T15:56:56.633995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:56:56.883996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2025-03-28T15:56:57.180996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:56:57.429002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

MonthlyIncome
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:57.728000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2025-03-28T15:56:58.071899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
6142 3
 
0.2%
2741 3
 
0.2%
2559 3
 
0.2%
2610 3
 
0.2%
2451 3
 
0.2%
5562 3
 
0.2%
3452 3
 
0.2%
2380 3
 
0.2%
6347 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

MonthlyRate
Real number (ℝ)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:58.411905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786
Coefficient of variation (CV)0.4972916
Kurtosis-1.2149561
Mean14313.103
Median Absolute Deviation (MAD)6206.5
Skewness0.018577808
Sum21040262
Variance50662878
MonotonicityNot monotonic
2025-03-28T15:56:59.019904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 3
 
0.2%
9150 3
 
0.2%
9558 2
 
0.1%
12858 2
 
0.1%
22074 2
 
0.1%
25326 2
 
0.1%
9096 2
 
0.1%
13008 2
 
0.1%
12355 2
 
0.1%
7744 2
 
0.1%
Other values (1417) 1448
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:59.330896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2025-03-28T15:56:59.577991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

PercentSalaryHike
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:56:59.909992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2025-03-28T15:57:00.172987image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 209
14.2%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 209
14.2%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%

PerformanceRating
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Length

2025-03-28T15:57:00.446981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:57:00.674989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Length

2025-03-28T15:57:00.923575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:57:01.188575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

StockOptionLevel
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2025-03-28T15:57:01.471141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:57:01.712144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

TotalWorkingYears
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:57:02.006146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2025-03-28T15:57:02.327141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:57:02.587146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2025-03-28T15:57:02.819145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Length

2025-03-28T15:57:03.837145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T15:57:04.084182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

YearsAtCompany
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:57:04.372615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2025-03-28T15:57:04.687613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:57:04.962614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2025-03-28T15:57:05.243619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 372
25.3%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 372
25.3%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

YearsSinceLastPromotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:57:05.504494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2025-03-28T15:57:05.767493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-03-28T15:57:06.035554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2025-03-28T15:57:06.304593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

BusinessTravel_Travel_Frequently
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1193 
True
277 
ValueCountFrequency (%)
False 1193
81.2%
True 277
 
18.8%
2025-03-28T15:57:06.572752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

BusinessTravel_Travel_Rarely
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1043 
False
427 
ValueCountFrequency (%)
True 1043
71.0%
False 427
29.0%
2025-03-28T15:57:06.788558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Department_Research & Development
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
961 
False
509 
ValueCountFrequency (%)
True 961
65.4%
False 509
34.6%
2025-03-28T15:57:07.006557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Department_Sales
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1024 
True
446 
ValueCountFrequency (%)
False 1024
69.7%
True 446
30.3%
2025-03-28T15:57:07.231599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

EducationField_Life Sciences
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
864 
True
606 
ValueCountFrequency (%)
False 864
58.8%
True 606
41.2%
2025-03-28T15:57:07.452037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

EducationField_Marketing
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1311 
True
159 
ValueCountFrequency (%)
False 1311
89.2%
True 159
 
10.8%
2025-03-28T15:57:07.671044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

EducationField_Medical
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1006 
True
464 
ValueCountFrequency (%)
False 1006
68.4%
True 464
31.6%
2025-03-28T15:57:07.884060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

EducationField_Other
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1388 
True
 
82
ValueCountFrequency (%)
False 1388
94.4%
True 82
 
5.6%
2025-03-28T15:57:08.100044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1338 
True
 
132
ValueCountFrequency (%)
False 1338
91.0%
True 132
 
9.0%
2025-03-28T15:57:08.310038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
882 
False
588 
ValueCountFrequency (%)
True 882
60.0%
False 588
40.0%
2025-03-28T15:57:08.526182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

JobRole_Human Resources
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1418 
True
 
52
ValueCountFrequency (%)
False 1418
96.5%
True 52
 
3.5%
2025-03-28T15:57:08.803280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1211 
True
259 
ValueCountFrequency (%)
False 1211
82.4%
True 259
 
17.6%
2025-03-28T15:57:09.199276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

JobRole_Manager
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1368 
True
 
102
ValueCountFrequency (%)
False 1368
93.1%
True 102
 
6.9%
2025-03-28T15:57:09.489275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1325 
True
145 
ValueCountFrequency (%)
False 1325
90.1%
True 145
 
9.9%
2025-03-28T15:57:09.716311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

JobRole_Research Director
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1390 
True
 
80
ValueCountFrequency (%)
False 1390
94.6%
True 80
 
5.4%
2025-03-28T15:57:09.936238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1178 
True
292 
ValueCountFrequency (%)
False 1178
80.1%
True 292
 
19.9%
2025-03-28T15:57:10.148664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

JobRole_Sales Executive
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1144 
True
326 
ValueCountFrequency (%)
False 1144
77.8%
True 326
 
22.2%
2025-03-28T15:57:10.366665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

JobRole_Sales Representative
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1387 
True
 
83
ValueCountFrequency (%)
False 1387
94.4%
True 83
 
5.6%
2025-03-28T15:57:10.580665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

MaritalStatus_Married
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
797 
True
673 
ValueCountFrequency (%)
False 797
54.2%
True 673
45.8%
2025-03-28T15:57:10.790663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

MaritalStatus_Single
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1000 
True
470 
ValueCountFrequency (%)
False 1000
68.0%
True 470
32.0%
2025-03-28T15:57:11.058665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False 1054
71.7%
True 416
 
28.3%
2025-03-28T15:57:11.277665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Interactions

2025-03-28T15:56:45.086561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:52.144670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:55.631902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:59.374343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:03.025326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:07.133705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:10.908224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:14.705024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:18.377084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:21.863869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:25.493732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:29.328413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:33.569657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:37.426760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:41.207564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:45.339563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:52.376670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:55.869900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:59.595446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:03.240335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:07.366325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:11.141245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:14.926023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:18.587955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:22.086879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:25.730735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:29.544411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:33.794657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:37.653138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:41.428562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:45.604562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:52.603678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:56.102901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:59.840450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:03.475327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:07.678325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:11.388227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:15.188027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:18.831991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:22.328726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:25.979734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:29.779412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:34.188657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:37.899822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:41.670564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:45.870565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:52.831708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:56.338902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:00.086449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:03.708343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:07.925328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:11.632031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:15.419027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:19.068952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:22.563732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:26.223735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:30.034411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:34.437655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:38.148819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:41.914570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:46.107561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:53.042672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:56.562385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:00.313453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:03.913336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:08.157324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:11.862735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:15.658025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:19.284910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:22.791727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:26.453731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:30.252560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:34.660655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:38.431815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:42.143561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:46.369560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:53.280306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:56.823490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:00.566448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:04.165335image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:08.403478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:12.125685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:15.928025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:19.528688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:23.046764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:26.705732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:30.503562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:34.918664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:38.683236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:42.396573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:46.636569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:53.514300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:57.085489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:00.821450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:04.446333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:08.660481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:12.379681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:16.162798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:19.779086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:23.303735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:26.978730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:30.757384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:35.181331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:38.938235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:42.650559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:46.869567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:53.724368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:57.332495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:01.050708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:04.655419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:08.891478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:12.615678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:16.376771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:19.991099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:23.529732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:27.213732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:30.986208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:35.412742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:39.179234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:42.877559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:47.109602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:53.939626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:57.596492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:01.276706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:04.871223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:09.124478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:12.848678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:16.581777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:20.204083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:23.760776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:27.447038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:31.199174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:35.643739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:39.407553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:43.160559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:47.358560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:54.180392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:57.834494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:01.519707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:05.113321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:09.362477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:13.103679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:16.862800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:20.431086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:23.993740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:27.691039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:31.441165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:35.899742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:39.660563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:43.426563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:47.621601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:54.419645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:58.103478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:01.771706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:05.360328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:09.619478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:13.373679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:17.110776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:20.674605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:24.248732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:27.948039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:32.329175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:36.155740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:39.919645image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:43.677558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:47.871164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:54.654852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:58.349668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:02.008706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:05.589107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:09.865133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:13.629302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:17.339772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:20.907599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:24.484732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:28.267036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:32.560167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:36.396738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:40.171631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:43.927562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:48.134778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:54.901858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:58.607969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:02.261707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:05.843227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:10.126386image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:13.909167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:17.578451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:21.152604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:24.737737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:28.533043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:32.819168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:36.644755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:40.431631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:44.195562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:48.386053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:55.144852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:58.867388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:02.510707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:06.087226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:10.383224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:14.178146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:17.872453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:21.393594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:24.995733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:28.788038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:33.075655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:36.903739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:40.681571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:44.451561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:48.699054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:55.390905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:55:59.121350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:02.771049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:06.327707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:10.634225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:14.437134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:18.129452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:21.624593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:25.237732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:29.055798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:33.320657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:37.165746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:40.947561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2025-03-28T15:56:44.708562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2025-03-28T15:57:11.569743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
AgeAttritionBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyDailyRateDepartment_Research & DevelopmentDepartment_SalesDistanceFromHomeEducationEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeEmployeeNumberEnvironmentSatisfactionGender_MaleHourlyRateJobInvolvementJobLevelJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeJobSatisfactionMaritalStatus_MarriedMaritalStatus_SingleMonthlyIncomeMonthlyRateNumCompaniesWorkedOverTime_YesPercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
Age1.0000.2130.0580.0480.0070.0000.000-0.0190.1530.0270.0000.0000.0420.033-0.0020.0060.0000.0290.0250.2950.0360.1400.3130.0910.2000.1650.1120.2280.0000.1130.1960.4720.0170.3530.0000.0080.0000.0350.0930.6570.0000.0330.2520.1980.1740.195
Attrition0.2131.0000.1100.0400.0620.0790.0740.0670.0000.0160.0460.0370.0000.0610.0000.1150.0090.0440.1320.2160.0170.0920.0750.0760.0810.0000.0000.1510.0990.0850.1720.2170.0100.1070.2430.0000.0000.0390.1980.2080.0790.0950.1730.1690.0270.179
BusinessTravel_Travel_Frequently0.0580.1101.0000.7510.0390.0000.0000.0340.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0450.0000.0130.0000.0470.0000.0000.0090.0030.0000.0000.0000.0170.0000.0000.0140.0000.0630.089
BusinessTravel_Travel_Rarely0.0480.0400.7511.0000.0000.0000.0000.0440.0000.0120.0190.0000.0000.0000.0000.0000.0000.0000.0130.0310.0000.0000.0000.0000.0170.0000.0000.0000.0020.0470.0000.0380.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.090
DailyRate0.0070.0620.0390.0001.0000.0000.000-0.0030.0170.0330.0620.0140.0000.086-0.0520.0000.0310.0240.0160.0000.0000.0350.0000.0000.0360.0000.0000.0000.0000.1080.0880.016-0.0320.0370.0000.0250.0000.0000.0400.021-0.0110.012-0.0100.007-0.038-0.005
Department_Research & Development0.0000.0790.0000.0000.0001.0000.9050.0000.0000.1230.4760.1800.0560.0250.0430.0220.0000.0000.0000.2470.2580.3340.0630.2370.1700.3600.7320.3320.0000.0000.0000.2270.0000.0000.0000.0480.0160.0250.0000.0200.0350.0540.0000.0000.0000.000
Department_Sales0.0000.0740.0000.0000.0000.9051.0000.0000.0000.0970.5250.1640.0550.0120.0000.0000.0160.0000.0250.2970.1200.3020.0190.2140.1530.3260.8070.3670.0000.0000.0180.2620.0000.0000.0000.0000.0130.0320.0000.0000.0440.0390.0000.0000.0000.000
DistanceFromHome-0.0190.0670.0340.044-0.0030.0000.0001.0000.0000.0000.0000.0000.0000.0210.0390.0000.0300.0200.0280.0540.0000.0000.0410.0000.0440.0000.0230.0000.0000.0000.0060.0030.040-0.0100.0660.0300.0580.0250.015-0.003-0.0250.0000.0110.014-0.0050.004
Education0.1530.0000.0000.0000.0170.0000.0000.0001.0000.0000.0640.0610.0720.0000.0450.0190.0000.0000.0000.0880.0000.0430.0000.0000.0580.0000.0470.0940.0150.0000.0000.0940.0370.1010.0010.0210.0000.0160.0270.0950.0270.0000.0710.0290.0000.000
EducationField_Life Sciences0.0270.0160.0130.0120.0330.1230.0970.0000.0001.0000.2880.5670.1990.2590.0000.0170.0000.0040.0000.0000.0530.0340.0000.0420.0000.0330.0860.0310.0440.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0430.0000.0140.0000.000
EducationField_Marketing0.0000.0460.0000.0190.0620.4760.5250.0000.0640.2881.0000.2330.0760.1020.0350.0000.0000.0750.0290.1720.0550.1560.0000.1080.0740.1690.4540.1260.0550.0000.0000.1500.0030.0720.0000.0000.0000.0670.0000.0590.0550.0090.0000.0510.0000.029
EducationField_Medical0.0000.0370.0000.0000.0140.1800.1640.0000.0610.5670.2331.0000.1600.2090.0000.0410.0000.0000.0000.0350.0290.0590.0000.0200.0540.0280.1290.0410.0000.0000.0000.0160.0000.0550.0000.0000.0000.0370.0530.0660.0940.0180.0140.0000.0000.000
EducationField_Other0.0420.0000.0000.0000.0000.0560.0550.0000.0720.1990.0760.1601.0000.0660.0370.0500.0000.0370.0000.0000.0000.0480.0000.0000.0000.0000.0210.0080.0000.0000.0000.0550.0510.0530.0000.0000.0000.0000.0000.0390.0260.0050.0180.0000.0000.000
EducationField_Technical Degree0.0330.0610.0000.0000.0860.0250.0120.0210.0000.2590.1020.2090.0661.0000.0000.0000.0000.0150.0000.0690.0000.0000.0220.0000.0000.0680.0500.0450.0000.0000.0000.0410.0000.0130.0000.0320.0000.0390.0000.0000.0450.0080.0000.0000.0000.000
EmployeeNumber-0.0020.0000.0000.000-0.0520.0430.0000.0390.0450.0000.0350.0000.0370.0001.0000.0000.0500.0350.0350.0360.0770.0000.0320.0420.0000.0000.0000.0000.0000.0000.0000.0020.0120.0070.016-0.0080.0290.0550.068-0.0040.0270.0000.013-0.0010.008-0.005
EnvironmentSatisfaction0.0060.1150.0000.0000.0000.0220.0000.0000.0190.0170.0000.0410.0500.0000.0001.0000.0000.0000.0340.0000.0240.0000.0000.0390.0310.0000.0000.0320.0000.0510.0180.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0000.0310.0360.0000.000
Gender_Male0.0000.0090.0000.0000.0310.0000.0160.0300.0000.0000.0000.0000.0000.0000.0500.0001.0000.0000.0000.0480.0190.0610.0170.0570.0000.0000.0000.0000.0000.0000.0170.0460.0000.0000.0310.0490.0000.0000.0000.0000.0000.0000.0660.0790.0000.000
HourlyRate0.0290.0440.0000.0000.0240.0000.0000.0200.0000.0040.0750.0000.0370.0150.0350.0000.0001.0000.0000.0000.0530.0000.0770.0000.0380.0000.0000.0000.0100.0390.051-0.020-0.0150.0190.064-0.0100.0000.0000.052-0.0120.0000.000-0.029-0.034-0.052-0.014
JobInvolvement0.0250.1320.0000.0130.0160.0000.0250.0280.0000.0000.0290.0000.0000.0000.0350.0340.0000.0001.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0150.0300.0460.0000.0000.0000.0360.0000.0000.0220.0000.0130.0000.0530.0000.0000.044
JobLevel0.2950.2160.0000.0310.0000.2470.2970.0540.0880.0000.1720.0350.0000.0690.0360.0000.0480.0000.0001.0000.1010.3960.6570.2870.4490.4540.4830.2730.0000.0280.0780.8640.0160.1130.0000.0000.0000.0000.0690.5390.0170.0000.3530.2410.2060.232
JobRole_Human Resources0.0360.0170.0000.0000.0000.2580.1200.0000.0000.0530.0550.0290.0000.0000.0770.0240.0190.0530.0000.1011.0000.0800.0370.0510.0270.0870.0940.0290.0370.0080.0410.0780.0000.0660.0000.0000.0000.0210.0000.0390.0000.0570.0000.0000.0000.000
JobRole_Laboratory Technician0.1400.0920.0000.0000.0350.3340.3020.0000.0430.0340.1560.0590.0480.0000.0000.0000.0610.0000.0000.3960.0801.0000.1200.1480.1040.2270.2430.1060.0000.0000.0000.3790.0000.0680.0340.0000.0000.0590.0320.2150.0000.0150.1320.1230.0910.089
JobRole_Manager0.3130.0750.0290.0000.0000.0630.0190.0410.0000.0000.0000.0000.0000.0220.0320.0000.0170.0770.0000.6570.0370.1201.0000.0820.0540.1300.1400.0550.0000.0390.0450.7270.0000.0850.0000.0680.0110.0000.0800.5720.0000.0000.4150.1930.2480.207
JobRole_Manufacturing Director0.0910.0760.0000.0000.0000.2370.2140.0000.0000.0420.1080.0200.0000.0000.0420.0390.0570.0000.0000.2870.0510.1480.0821.0000.0700.1600.1720.0710.0060.0000.0000.2720.0000.0380.0000.0000.0050.0290.0000.1030.0330.0000.0610.0590.0330.072
JobRole_Research Director0.2000.0810.0000.0170.0360.1700.1530.0440.0580.0000.0740.0540.0000.0000.0000.0310.0000.0380.0000.4490.0270.1040.0540.0701.0000.1130.1220.0450.0000.0000.0290.5460.0000.1200.0000.0000.0180.0000.0000.3300.0000.0000.1710.1850.1000.153
JobRole_Research Scientist0.1650.0000.0000.0000.0000.3600.3260.0000.0000.0330.1690.0280.0000.0680.0000.0000.0000.0000.0280.4540.0870.2270.1300.1600.1131.0000.2630.1150.0000.0280.0450.4050.0000.0710.0460.0410.0000.0260.0210.2560.0390.0600.1400.1480.1090.119
JobRole_Sales Executive0.1120.0000.0000.0000.0000.7320.8070.0230.0470.0860.4540.1290.0210.0500.0000.0000.0000.0000.0000.4830.0940.2430.1400.1720.1220.2631.0000.1240.0230.0000.0000.4620.0000.0420.0000.0000.0290.0470.0180.2130.0000.0000.1550.1240.0610.089
JobRole_Sales Representative0.2280.1510.0450.0000.0000.3320.3670.0000.0940.0310.1260.0410.0080.0450.0000.0320.0000.0000.0000.2730.0290.1060.0550.0710.0450.1150.1241.0000.0190.0000.0640.2910.0000.1060.0000.0370.0000.0000.0250.3390.0500.0320.1990.1430.0430.155
JobSatisfaction0.0000.0990.0000.0020.0000.0000.0000.0000.0150.0440.0550.0000.0000.0000.0000.0000.0000.0100.0000.0000.0370.0000.0000.0060.0000.0000.0230.0191.0000.0000.0000.0000.0480.0000.0220.0000.0260.0000.0000.0240.0210.0000.0000.0000.0000.000
MaritalStatus_Married0.1130.0850.0130.0470.1080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0390.0150.0280.0080.0000.0390.0000.0000.0280.0000.0000.0001.0000.6280.0440.0270.0260.0000.0000.0000.0250.3950.0650.0320.0000.0080.0540.0680.000
MaritalStatus_Single0.1960.1720.0000.0000.0880.0000.0180.0060.0000.0000.0000.0000.0000.0000.0000.0180.0170.0510.0300.0780.0410.0000.0450.0000.0290.0450.0000.0640.0000.6281.0000.0810.0000.0550.0000.0000.0000.0080.7900.1130.0000.0000.0330.0800.0000.048
MonthlyIncome0.4720.2170.0470.0380.0160.2270.2620.0030.0940.0000.1500.0160.0550.0410.0020.0000.046-0.0200.0460.8640.0780.3790.7270.2720.5460.4050.4620.2910.0000.0440.0811.0000.0540.1900.000-0.0340.0000.0430.0560.710-0.0350.0000.4640.3950.2650.365
MonthlyRate0.0170.0100.0000.000-0.0320.0000.0000.0400.0370.0000.0030.0000.0510.0000.0120.0000.000-0.0150.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0480.0270.0000.0541.0000.0200.000-0.0050.0150.0550.0000.013-0.0100.034-0.030-0.007-0.016-0.035
NumCompaniesWorked0.3530.1070.0000.0000.0370.0000.000-0.0100.1010.0910.0720.0550.0530.0130.0070.0000.0000.0190.0000.1130.0660.0680.0850.0380.1200.0710.0420.1060.0000.0260.0550.1900.0201.0000.0000.0000.0000.0000.0000.315-0.0470.051-0.171-0.128-0.067-0.144
OverTime_Yes0.0000.2430.0090.0000.0000.0000.0000.0660.0010.0000.0000.0000.0000.0000.0160.0600.0310.0640.0000.0000.0000.0340.0000.0000.0000.0460.0000.0000.0220.0000.0000.0000.0000.0001.0000.0000.0000.0250.0000.0000.0990.0000.0180.0420.0110.000
PercentSalaryHike0.0080.0000.0030.0600.0250.0480.0000.0300.0210.0000.0000.0000.0000.032-0.0080.0000.049-0.0100.0360.0000.0000.0000.0680.0000.0000.0410.0000.0370.0000.0000.000-0.034-0.0050.0000.0001.0000.9970.0270.000-0.026-0.0040.000-0.054-0.026-0.055-0.026
PerformanceRating0.0000.0000.0000.0000.0000.0160.0130.0580.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0110.0050.0180.0000.0290.0000.0260.0000.0000.0000.0150.0000.0000.9971.0000.0000.0000.0000.0000.0000.0000.0310.0000.030
RelationshipSatisfaction0.0350.0390.0000.0000.0000.0250.0320.0250.0160.0000.0670.0370.0000.0390.0550.0000.0000.0000.0000.0000.0210.0590.0000.0290.0000.0260.0470.0000.0000.0250.0080.0430.0550.0000.0250.0270.0001.0000.0300.0310.0000.0000.0000.0000.0500.000
StockOptionLevel0.0930.1980.0000.0000.0400.0000.0000.0150.0270.0000.0000.0530.0000.0000.0680.0000.0000.0520.0220.0690.0000.0320.0800.0000.0000.0210.0180.0250.0000.3950.7900.0560.0000.0000.0000.0000.0000.0301.0000.0640.0000.0190.0120.0230.0560.030
TotalWorkingYears0.6570.2080.0170.0000.0210.0200.000-0.0030.0950.0000.0590.0660.0390.000-0.0040.0000.000-0.0120.0000.5390.0390.2150.5720.1030.3300.2560.2130.3390.0240.0650.1130.7100.0130.3150.000-0.0260.0000.0310.0641.000-0.0140.0000.5940.4930.3350.495
TrainingTimesLastYear0.0000.0790.0000.000-0.0110.0350.044-0.0250.0270.0000.0550.0940.0260.0450.0270.0000.0000.0000.0130.0170.0000.0000.0000.0330.0000.0390.0000.0500.0210.0320.000-0.035-0.010-0.0470.099-0.0040.0000.0000.000-0.0141.0000.0000.0010.0050.010-0.012
WorkLifeBalance0.0330.0950.0000.0000.0120.0540.0390.0000.0000.0430.0090.0180.0050.0080.0000.0000.0000.0000.0000.0000.0570.0150.0000.0000.0000.0600.0000.0320.0000.0000.0000.0000.0340.0510.0000.0000.0000.0000.0190.0000.0001.0000.0200.0250.0000.031
YearsAtCompany0.2520.1730.0140.000-0.0100.0000.0000.0110.0710.0000.0000.0140.0180.0000.0130.0310.066-0.0290.0530.3530.0000.1320.4150.0610.1710.1400.1550.1990.0000.0080.0330.464-0.030-0.1710.018-0.0540.0000.0000.0120.5940.0010.0201.0000.8540.5200.843
YearsInCurrentRole0.1980.1690.0000.0000.0070.0000.0000.0140.0290.0140.0510.0000.0000.000-0.0010.0360.079-0.0340.0000.2410.0000.1230.1930.0590.1850.1480.1240.1430.0000.0540.0800.395-0.007-0.1280.042-0.0260.0310.0000.0230.4930.0050.0250.8541.0000.5060.725
YearsSinceLastPromotion0.1740.0270.0630.000-0.0380.0000.000-0.0050.0000.0000.0000.0000.0000.0000.0080.0000.000-0.0520.0000.2060.0000.0910.2480.0330.1000.1090.0610.0430.0000.0680.0000.265-0.016-0.0670.011-0.0550.0000.0500.0560.3350.0100.0000.5200.5061.0000.467
YearsWithCurrManager0.1950.1790.0890.090-0.0050.0000.0000.0040.0000.0000.0290.0000.0000.000-0.0050.0000.000-0.0140.0440.2320.0000.0890.2070.0720.1530.1190.0890.1550.0000.0000.0480.365-0.035-0.1440.000-0.0260.0300.0000.0300.495-0.0120.0310.8430.7250.4671.000

Missing values

2025-03-28T15:56:49.227053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-28T15:56:50.664045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionDailyRateDistanceFromHomeEducationEmployeeNumberEnvironmentSatisfactionHourlyRateJobInvolvementJobLevelJobSatisfactionMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyDepartment_Research & DevelopmentDepartment_SalesEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_MaleJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_Yes
041Yes11021212943245993194798113108016405FalseTrueFalseTrueTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueTrue
149No279812361222513024907123441103310717TrueFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseFalse
237Yes1373224492213209023966153207330000FalseTrueTrueFalseFalseFalseFalseTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueTrue
333No13923454563132909231591113308338730TrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseTrueFalseTrue
427No5912171403123468166329123416332222FalseTrueTrueFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalse
532No10052284793143068118640133308227736TrueFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalse
659No132433103814112670996442041312321000FalseTrueTrueFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseTrue
730No1358241114673132693133351224211231000FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalse
838No216233124442339526878702142010239718TrueFalseTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
936No12992731339432352371657761332217327777FalseTrueTrueFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
AgeAttritionDailyRateDistanceFromHomeEducationEmployeeNumberEnvironmentSatisfactionHourlyRateJobInvolvementJobLevelJobSatisfactionMonthlyIncomeMonthlyRateNumCompaniesWorkedPercentSalaryHikePerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManagerBusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyDepartment_Research & DevelopmentDepartment_SalesEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeGender_MaleJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_Yes
146029No4682842054473211378584891143205315404FalseTrueTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrueFalse
146150Yes4102832055439231108541658641332120333220FalseTrueFalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseTrue
146239No7222412056260244120318828011311212220996FalseTrueFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalse
146331No3255320572743219936378701932010239417FalseFalseTrueFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseTrueFalse
146426No11675320604302132966213780183405234200FalseTrueFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseTrueFalse
146536No884232206134142425711229041733117335203TrueFalseTrueFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalse
146639No6136120624422319991214574153119537717FalseTrueTrueFalseFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalse
146727No155432064287422614251741204216036203FalseTrueTrueFalseTrueFalseFalseFalseFalseTrueFalseFalseFalseTrueFalseFalseFalseFalseTrueFalseTrue
146849No102323206546322253901324321434017329608TrueFalseFalseTrueFalseFalseTrueFalseFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseTrueFalseFalse
146934No6288320682824234404102282123106344312FalseTrueTrueFalseFalseFalseTrueFalseFalseTrueFalseTrueFalseFalseFalseFalseFalseFalseTrueFalseFalse